How do I use a taxonomy and the support vector machine for question classification?
This is not an answer (I don't have enough reputation to comment). I did something close to this in my master's thesis and think it is close to what you are interested in.

In it, I had developed a framework for extracting metadata from web-based educational content. This metadata was used for classifying the the educaitonal content for many different attributes, which could then be used for faster and more efficient search and discovery of educational content. 

The educational resources (containing the content) could be anything like text or PDFs of assignments, homework, assignments, online books, exam questions, courses etc (which many colleges host online). To identify what kind of educational resource it is, I would parse the text and look for keywords and formatting styles (preprocessing included constructing 2-grams and 3-grams, POS tagging, using small specific parsers for NER, dates and other text entities one encounters in educational content). 

For some part I used Wordnet (also available under python-nltk) to obtain relationships between different entities and also to find closeness between them. DBpedia was also used. However, for the most part I had to identify the most commonly occuring terms and build a taxonomy by hand. (It took a lot of time!). I obtained a lot of candidates for keywords by looking at openly available taxonomies.

For extracting domain specific taxonomy/ontology, one needs manually to build it. Ontology generation from text is an active area of research and building domain-specific ontology has been tried for many years. One example of such taxonomy (here thesaurus) is agrovoc where domain experts have contributed to the knowledge by identifying agricultural entities manually. 

There are a lot of places where domain specific vocabulary is available; maybe you can use that. In some aspects it is close to supervised machine learning, where one has some nice data and correspondingly nice output. However, on my part, there wasn't much learning in it - more like template matching. 

Hope this helps.